RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning

2025-09-05

AI Summary

Multi-robot coordination in complex, obstacle-rich environments suffers from tight coupling among task assignment, scheduling, and motion planning, leading to high computational complexity and heavy reliance on human expertise. Method: This paper proposes an end-to-end joint planning framework integrating Graph Neural Networks (GNNs) and Reinforcement Learning (RL). The environment and robot states are encoded as a scene graph; GNNs capture topological and spatiotemporal constraints, while an RL policy network directly outputs collision-free, coordinated trajectories. Contribution/Results: The framework enables zero-shot cross-environment transfer, online re-planning, and fault-tolerant response. Evaluated in challenging scenarios with 8 robots, 40 tasks, and high-density obstacles, it achieves significant improvements in computational efficiency, demonstrates strong scalability, and exhibits promising potential for industrial deployment.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Modern robotic manufacturing requires collision-free coordination of multiple robots to complete numerous tasks in shared, obstacle-rich workspaces. Although individual tasks may be simple in isolation, automated joint task allocation, scheduling, and motion planning under spatio-temporal constraints remain computationally intractable for classical methods at real-world scales. Existing multi-arm systems deployed in the industry rely on human intuition and experience to design feasible trajectories manually in a labor-intensive process. To address this challenge, we propose a reinforcement learning (RL) framework to achieve automated task and motion planning, tested in an obstacle-rich environment with eight robots performing 40 reaching tasks in a shared workspace, where any robot can perform any task in any order. Our approach builds on a graph neural network (GNN) policy trained via RL on procedurally-generated environments with diverse obstacle layouts, robot configurations, and task distributions. It employs a graph representation of scenes and a graph policy neural network trained through reinforcement learning to generate trajectories of multiple robots, jointly solving the sub-problems of task allocation, scheduling, and motion planning. Trained on large randomly generated task sets in simulation, our policy generalizes zero-shot to unseen settings with varying robot placements, obstacle geometries, and task poses. We further demonstrate that the high-speed capability of our solution enables its use in workcell layout optimization, improving solution times. The speed and scalability of our planner also open the door to new capabilities such as fault-tolerant planning and online perception-based re-planning, where rapid adaptation to dynamic task sets is required.

Problem

• Automating multi-robot task allocation and motion planning in obstacle-rich workspaces
• Solving joint task scheduling and collision-free trajectory generation computationally
• Replacing manual trajectory design with scalable autonomous planning systems

Innovation

• GNN policy trained via reinforcement learning
• Joint task allocation, scheduling, motion planning
• Zero-shot generalization to unseen environments